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A career as a heavy equipment operator
Heavy equipment operators, who drive the cranes, excavators, and other large machinery that power construction and engineering projects, are always in demand. Whether building skyscrapers, repairing aging infrastructure, or mining oil and gas fields, a career as a heavy equipment operator can be a rewarding and dependable occupation. Readers will find valuable information on the duties, skills, training, and future outlook of this exciting construction career. Other topics explored include opportunities for women and minorities, the use of green technology, and insight into some of the most ambitious construction projects at work today. Bibliography, Detailed Table of Contents, For Further Information Section, Glossary, Index, Sidebars, Web Sites, Full-color photographs.
Book
Antenna Design for Mobile Devices
Written by an antenna engineer turned professor who has worked at Apple, Nokia and Amphenol,<i>Antenna Design for Mobile Devices</i>is a comprehensive guide for fresh and intermediate engineers involved in antenna design. The book instructs readers through all aspects of real world antenna designs, which includes how to make a stable antenna fixture, designing various types of antennas, designing an antenna with good manufacturability, using various matching technique to improve antenna performance, setting up production measurement for mass manufacturing, and making antenna SAR and HAC compliant. Most popular antenna categories, such as internal PIFA, integral IFA, internal folded monopole, ceramic antennas, stubby antennas and whip stubby antennas, are introduced in the book. The book focuses on the basic principle of each kind of antenna and emphasizes on key parameters of antenna optimization. Complimentary matching software, which accompanies the book, is provided so readers can practice various antenna matching technique and design matching circuits for real projects. <ul> <li>A one-stop design reference containing all an engineer needs when designing antennas</li> <li>Accessible to readers of many levels, from introductory to specialist</li> <li>Presents shortcuts for engineers who lack antenna knowledge but need no-hassle techniques for designing simple antennas</li> <li>Contains hands-on knowledge not available in other books</li> <li>Written by a practicing expert who has hired and trained numerous engineers</li> <li>Incorporates the various techniques used by pure-play antenna firms, established mobile device brands, and new entrants to the mobile space</li> <li>Comes with antenna matching software written by the author, which can be used for practice and real-world projects</li> <li>Presentation slides with lecture notes available for instructor use</li> </ul> <p>This book is targeted at practicing antenna engineers, particularly those focusing on mobile devices, as well as researchers and academics looking to keep up with this quick-changing field. Engineering managers will find it to be a helpful guide for teaching new hires, while new hires, by using the book themselves, will be able to quickly gain expert-level proficiencies. The book is also suitable for wireless network equipment engineers, who desire a stronger sense of antenna principles, as well as electronic engineering students studying electromagnetics. Readers should possess a basic undergraduate-level understanding of electromagnetic theory.<br /> Companion website for the book:<br /> <a href=\"http://www.wiley.com/go/zhangantenna\">http://www.wiley.com/go/zhangantenna</a></p>
eBook
Research and Application of YOLOv11-Based Object Segmentation in Intelligent Recognition at Construction Sites
With the increasing complexity of construction site environments, robust object detection and segmentation technologies are essential for enhancing intelligent monitoring and ensuring safety. This study investigates the application of YOLOv11-Seg, an advanced target segmentation technology, for intelligent recognition on construction sites. The research focuses on improving the detection and segmentation of 13 object categories, including excavators, bulldozers, cranes, workers, and other equipment. The methodology involves preparing a high-quality dataset through cleaning, annotation, and augmentation, followed by training the YOLOv11-Seg model over 351 epochs. The loss function analysis indicates stable convergence, demonstrating the model’s effective learning capabilities. The evaluation results show an mAP@0.5 average of 0.808, F1 Score(B) of 0.8212, and F1 Score(M) of 0.8382, with 81.56% of test samples achieving confidence scores above 90%. The model performs effectively in static scenarios, such as equipment detection in Xiong’an New District, and dynamic scenarios, including real-time monitoring of workers and vehicles, maintaining stable performance even at 1080P resolution. Furthermore, it demonstrates robustness under challenging conditions, including nighttime, non-construction scenes, and incomplete images. The study concludes that YOLOv11-Seg exhibits strong generalization capability and practical utility, providing a reliable foundation for enhancing safety and intelligent monitoring at construction sites. Future work may integrate edge computing and UAV technologies to support the digital transformation of construction management.
Journal Article
Construction vehicles
\"Simple text and full-color photographs describe eight different vehicles used in construction work.\"-- Provided by publisher.
Book
Semantic Segmentation of Heavy Construction Equipment Based on Point Cloud Data
Most of the currently developed 3D point cloud data-based object recognition algorithms have been designed for small indoor objects, posing challenges when applied to large-scale 3D point cloud data in outdoor construction sites. To address this issue, this research selected four high-performance deep learning-based semantic segmentation algorithms for large-scale 3D point cloud data: Rand-LA-Net, KPConv Rigid, KPConv Deformable, and SCF-Net. These algorithms were trained and validated using 3D digital maps of earthwork sites to build semantic segmentation models, and their performance was tested and evaluated. The results of this research represent the first application of 3D semantic segmentation algorithms to large-scale 3D digital maps of earthwork sites. It was experimentally confirmed that object recognition technology can be implemented in the construction industry using 3D digital maps composed of large-scale 3D point cloud data.
Journal Article
Diggersaurs
Bigger than diggers or dinosaurs, a crew of diggersaurs rumble, crunch, and stack their way through a busy day at the construction site.
Book
Sensor Acquisition and Allocation for Real-Time Monitoring of Articulated Construction Equipment in Digital Twins
The visibility available to an equipment operator on a dynamic construction site can often be blocked by various obstacles such as materials, temporary or permanent facilities, other equipment, and workers. Equipment monitoring in real-time digital twins can thus play a crucial role in accident prevention. This paper develops a scalable technical approach and presents a prototype application framework for transmitting real world sensor data to update 3D equipment models inside a graphical digital twin for concurrent visualization of a monitored construction operation. The developed framework and workflow can be extended to visualize any construction operation, as it occurs, inside a dynamic 3D world simply by outfitting the real equipment with appropriate sensors and connecting them to their virtual counterparts. The implemented proof-of-concept interface is described in the context of a real-time 3D digital twin for assisting excavator operators prevent unintended strikes with underground utilities. Experiments to validate the proposed technical approach by simulating the real-time motion of a backhoe loader’s articulated arm using orientation sensors installed on its boom, stick, and bucket are described. The experimental results characterize the scope and potential reasons for spatio-temporal discrepancies that can occur between a monitored real operation and its replicated digital twin. The effect of an operator warning mechanism based on preset safety thresholds is also investigated and described.
Journal Article
Digger's busy day
It's busy in the building site, with bulldozers, cranes, tip-up trucks and cement mixers all at work! Dive into the world of diggers and trucks with this picture book brimming with busy bright photos, accessible read out loud text and playful rhymes.
Book
Intelligent Fleet Monitoring System for Productivity Management of Earthwork Equipment
Earthwork operations constitute a substantial share of infrastructure project costs and are critical to overall project efficiency. However, the construction industry still relies on conventional approaches and there is a lack of integrated fleet management systems for collaboratively working equipment. While telematics is widely used in other industries, its applications to monitor the complex interactions between excavators, dump trucks, and dozers in real time remain limited. This study proposes an intelligent fleet monitoring system that utilizes only satellite navigation data (GNSS) to analyze the real-time productivity of multiple earthwork machines without relying on additional sensors, such as IMU or accelerometers, thereby eliminating the need for separate measurement procedures. A lightweight site configuration step is required to define the work area/loading/dumping geofences on an existing site map. This research provides novel developed algorithms that facilitate a real-time productivity assessment for several earthwork equipment and provide planning-level recommendations for equipment deployment combinations. Dedicated motion classification algorithms were developed for excavators, dump trucks, and dozers to distinguish activity states, to compute working and idle times, and to quantify operational efficiency. The system integrates a web-based e-Fleet Management platform and a mobile e-Map application for visualization and equipment optimization. Field validation was conducted on two active earthwork projects to evaluate accuracy and feasibility. The results demonstrate that the developed algorithms achieved classification and productivity estimation errors within 2.5%, while enabling optimized equipment combinations and improved cycle time efficiency. The proposed system offers a practical, sensor-independent approach for enhancing productivity monitoring, real-time decision-making, and cost efficiency in large-scale earthwork operations.
Journal Article